Hybrid classification of pulmonary nodules
Date
2009
Authors
Lee, S.
Kouzani, A.
Hu, E.
Editors
Cai, Z.H.
Li, Z.H.
Kang, Z.
Liu, Y.
Li, Z.H.
Kang, Z.
Liu, Y.
Advisors
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Type:
Conference paper
Citation
Computational Intelligence and Intelligent Systems: 4th International Symposium on Intelligence Computation and Applications, ISICA 2009, Huangshi, China, October 23-25 2009 / Z. Cai, Z. Li, Z. Kang and Y. Liu (eds.): pp.472-481
Statement of Responsibility
S. L. A. Lee, A. Z. Kouzani and E. J. Hu
Conference Name
ISICA (23 Oct 2009 - 25 Oct 2009 : Communications in Computer and Information Science)
Abstract
Automated classification of lung nodules is challenging because of the variation in shape and size of lung nodules, as well as their associated differences in their images. Ensemble based learners have demonstrated the potentialof good performance. Random forests are employed for pulmonary nodule classification where each tree in the forest produces a classification decision, and an integrated output is calculated. A classification aided by clustering approach is proposed to improve the lung nodule classification performance. Three experiments are performed using the LIDC lung image database of 32 cases. The classification performance and execution times are presented and discussed.
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© Springer-Verlag Berlin Heidelberg 2009